We measure the level of consensus in the network at termination time \(t_{\mathrm {max}}\le t_{c}\)  as \(\langle \mathrm {Cons} \rangle =\langle |\rho (t_{\mathrm {max}})-0.5|/0.5 \rangle \) , where the average is over 100 independent realizations and \(\rho \)  indicates the density of nodes in state \(s=1\) . The lowest level of consensus, \(\langle \mathrm {Cons} \rangle =0\) , happens when half of the network is in one state and the other half in the opposite. We compute consensus fluctuations i.e., the susceptibility, as \(\chi =(\langle \mathrm {Cons^2} \rangle -\langle \mathrm {Cons} \rangle ^2)/\langle \mathrm {Cons} \rangle \) , where the normalization factor is chosen following Ref. [1]}. Finally, we evaluate the survival probability, \(S\) , measuring the fraction of realizations that have not reached consensus up to \(t_{\mathrm {max}}=t_{c}\) . \(S\)  gives insight about how individual realizations contribute to the average level of consensus.

